Deadbeat Predictive Current Control Research Articles

Robust deadbeat predictive current control for unipolar sinusoidal excited SRM with multi-parameter mismatch compensation

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model’s nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC.

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

Robustness Improved Method for Deadbeat Predictive Current Control of PMLSM with Segmented Stators

Permanent magnet linear synchronous motors (PMLSMs) with stator segmented structures are widely used in the design of high-power propulsion systems. However, due to the inherent delay and segmented structure of the systems, there are parameter disturbances in the inductance and flux linkage of the motors. This makes the deadbeat predictive current control (DPCC) algorithm for a current loop less robust in the control system, leading to a decrease in control performance. Compensation methods such as compensation by observer and online estimation of parameters, are problematic to apply in practice due to the difficulty of parameter adjustment and the high complexity of the algorithm. In this paper, a robustness-improved incremental DPCC (RII-DPCC) method—which uses incremental DPCC (I-DPCC) to eliminate flux linkage parameters—is proposed. The stability of the current loop was evaluated through zero-pole analysis of the discrete transfer function. Current feedforward was introduced to improve the stability of I-DPCC. The inductance stability range of I-DPCC was increased from 0.8–1.25 times to 0–2 times the actual value, and the theoretical stability range was increased more than 4 times, effectively improving the robustness of the predictive model to flux linkage and inductance parameters. Finally, the effectiveness of the proposed method was verified through numerical simulation and experiment.

An Improved Deadbeat Predictive Current Control Based on Parameter Identification for PMSM

An Improved Deadbeat Predictive Current Control Based on Parameter Identification for PMSM

Improved Deadbeat Predictive Current Control With Extended State Observer for Dual Three-Phase PMSMs

Improved Deadbeat Predictive Current Control With Extended State Observer for Dual Three-Phase PMSMs

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

A New Sliding-Mode Observer-Based Deadbeat Predictive Current Control Method for Permanent Magnet Motor Drive

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

RLS-based deadbeat predictive current control for dual three-phase segmented powered linear motors

RLS-based deadbeat predictive current control for dual three-phase segmented powered linear motors

Enhanced deadbeat predictive current control with novel generalized space vector modulation for five‐level inverter fed permanent magnet synchronous motor drive with reduced torque ripples and less computational burden

SummaryTraditional electric drive control methodologies, such as field oriented control (FOC) and direct torque and flux control (DTFC), predominantly rely on multiple proportional‐integral (PI) controllers that demand complex tuning. While model predictive current control (MPCC) omits PI controllers, it has inherent challenges including high computational burden and tempered dynamics. Although two‐level inverters (2LI) are prevalent in many applications, their performance wanes in high‐power applications like high performance industrial drives and marine propulsions. Multilevel inverters (MLIs) are more efficient, but combined with space vector modulation (SVM), they add computational complexity, particularly at higher levels, limiting drive sampling frequencies and causing torque ripples and harmonics, affecting performance. To address the above drawbacks, this manuscript presents a deadbeat predictive current control of five‐level inverter fed permanent magnet synchronous motor (PMSM) drive with a novel generalized SVM algorithm with reduced computational burden and improved performance. The proposed method is validated through MATLAB/Simulink‐based simulation study and experimental implementation. The results are presented and discussed. The results are compared with DTFC and MPCC where it is observed that the proposed method offers superior performance with reduced computational burden and streamlined implementation.

Dual Model Predictive Control Strategy of Direct‐Drive PMSM Based on Sliding Mode Disturbance Observer

Aiming at the current control steady‐state error caused by sampling delay, parameters mismatch of direct‐drive permanent magnet synchronous motor (PMSM). This paper proposed a method of dual model predictive control (MPC) of direct‐drive PMSM based on sliding mode disturbance observer (SMDO). Deadbeat predictive current control (DPCC) is used for current loop, a SMDO is proposed to observe the system disturbance, the estimation is compensated to DPCC by feedback to decrease the current control steady‐state error. For speed loop, MPC with an internal model control observer (IMCO) can observe and compensate the load disturbance feedforward. The experimental results show that, the dynamic response speed of the proposed strategy is fast, the speed recovery time after sudden load torque is short, and the anti‐load disturbance performance and current tracking performance are effectively improved. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Simple Deadbeat Predictive Current Control for OW-PMSM Drives Based on Reference Voltage Redistribution

A Simple Deadbeat Predictive Current Control for OW-PMSM Drives Based on Reference Voltage Redistribution

Improved Deadbeat Predictive Current Control of PMSM Drives with Repetitive Control-Based Disturbance Correction Observer

Improved Deadbeat Predictive Current Control of PMSM Drives with Repetitive Control-Based Disturbance Correction Observer

Model Parameter Self-Correcting Deadbeat Predictive Current Control for SPMSM Drives

Model Parameter Self-Correcting Deadbeat Predictive Current Control for SPMSM Drives

Model-Free Deadbeat Predictive Current Control for SPMSM Based on Adaptive Gain Extended State Observer

Model-Free Deadbeat Predictive Current Control for SPMSM Based on Adaptive Gain Extended State Observer

Switching Active Disturbance Rejection Based Deadbeat Predictive Current Control for Permanent Magnet Synchronous Motors

Switching Active Disturbance Rejection Based Deadbeat Predictive Current Control for Permanent Magnet Synchronous Motors

Enhanced Deadbeat Predictive Current Control for PMSM Drives Using Iterative Sliding Mode Observer

Enhanced Deadbeat Predictive Current Control for PMSM Drives Using Iterative Sliding Mode Observer

Deadbeat Predictive Current Control for Surface-Mounted Permanent-Magnet Synchronous Motor Based on Weakened Integral Sliding Mode Compensation

Deadbeat predictive current control (DPCC) has excellent dynamics and can achieve current control with less computational effort. However, its control performance relies on the precision of the parameters of the motor. Current static error will be generated and control performance will be decreased when the predictive model parameters do not correspond to the practical parameters of the motor. In this article, a weakened integral sliding mode compensation method is proposed which converts the current error into a voltage compensation term and adds it to the prediction control output to effectively compensate for the steady-state error. In addition, a boundary layer is introduced to weaken the integral so as to solve the problems of integral saturation and overshoot caused by the introduction of the integral term when the error is large. Moreover, weight factors are introduced to optimize the feedback current, which enhances the robustness of the system. In the end, the effectiveness of this method is verified via simulation and experimental results.